Open AccessUsage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm
Author(s) -
G. Mallikarjuna Rao,
K. Anitha
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.h7168.129219
Subject(s) - random forest , decision tree , computer science , data mining , decision tree learning , field (mathematics) , tree (set theory) , algorithm , heart disease , machine learning , artificial intelligence , mathematics , medicine , mathematical analysis , pure mathematics , cardiology
Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.